Application Placement Research Articles

PL02 Presentation Time: 1:45 PM: Design, Construction, and Validation of an Electromagnetic Tracking-Based Guidance System for Assisted Placement of Interstitial Brachytherapy Applicators

PL02 Presentation Time: 1:45 PM: Design, Construction, and Validation of an Electromagnetic Tracking-Based Guidance System for Assisted Placement of Interstitial Brachytherapy Applicators

The effect of one-room CT guided brachytherapy on procedure time and cost in the treatment of cervical cancer

IntroductionBrachytherapy is associated with improved overall survival in cervical cancer patients, but the utilization seems hindered by high costs and relatively low reimbursement, particularly in the US. A one-room brachytherapy suite with CT (ORBT) could optimize the treatment workflow. By eliminating transport and waiting times, limiting applicator movement, and providing real-time applicator placement feedback, treatment time and costs could potentially be reduced. This study assesses the potential value of implementing ORBT in cervical cancer treatment. MethodsA variable cost model was developed to compare current (multi-room) brachytherapy workflows (MBRT) to ORBT, taking into account staff utilization, staff, equipment and consumables costs and room expenses. Two current care scenarios were simulated; applicator placement performed in the operating room (S1), and applicator placement performed in a brachytherapy suite (S2). For both scenarios literature reported fraction times of MBRT were compared to a range of ORBT times. Sensitivity analyses were performed to determine the influence of input parameters. ResultsIn scenario one, the results showed yearly savings of $45,572 up to $339,439 (USD), assuming a 5% and 20% reduction in fraction duration, respectively, in ORBT compared to MRBT. In scenario two, ORBT does not result in costs savings at 5% to 15% improvement. Therefore, only when ORBT results in a >20% improvement of fraction time, cost will be saved. DiscussionThe results indicate that reducing procedure time (using ORBT) can lead to cost savings, depending on the current workflow. Savings appear to depend mostly on applicator placement location, number of patients per year, and involved personnel.

Comprehensive commissioning of a cone beam CT imaging ring for treatment of HDR GYN patients

PurposeA new mobile cone beam computed tomography (CBCT) imaging ring (IRm, Elekta, v2.10.6, Veenendaal, Netherlands) has recently been proposed for brachytherapy to improve procedure efficiency. We describe the commissioning process and end-to-end tests for GYN HDR brachytherapy employing IRm CBCT imaging. Materials and MethodsCommissioning included imaging isocenter test, 3D image quality, 2D imaging quality, image dose, and tube characteristics. CIRS HDR GYN phantom and Venezia CT/MR gynecological applicator were used to perform end-to-end (E2E) tests and optimize workflow. Venezia applicator and four interstitial needles were inserted into the phantom and IRm CBCT images were acquired. Phantom and applicator were scanned with CT scanner (Siemens SOMATOM go.Open Pro) using department's pelvis imaging protocol. MR imaging was performed using 0.35T MR Linac TRUFI pulse sequence. CBCT images were registered to CT and MR using rigid registration to assess image quality and applicator geometry fidelity. ResultsAll physics tests passed within acceptance tolerances. Registration of CBCT images to MR and CT scans was acceptable for applicator placement. Applicator registration of CBCT images to CT demonstrated excellent agreement of most distal source dwell position (<1 mm). Slice thickness was also measured to be 1.25 mm, within 0.5 mm of its nominal value. ConclusionBased on E2E and commissioning results, IRm is an appropriate tool for brachytherapy treatment planning. This study demonstrated good image quality in GYN phantom and Venezia applicator using the IRm. Distal source dwell position agreement between CBCT and CT was acceptable for clinical use.

Multi-objective application placement in fog computing using graph neural network-based reinforcement learning

We propose a framework designed to tackle a multi-objective optimization challenge related to the placement of applications in fog computing, employing a deep reinforcement learning (DRL) approach. Unlike other optimization techniques, such as integer linear programming or genetic algorithms, DRL models are applied in real time to solve similar problem situations after training. Our model comprises a learning process featuring a graph neural network and two actor-critics, providing a holistic perspective on the priorities concerning interconnected services that constitute an application. The learning model incorporates the relationships between services as a crucial factor in placement decisions: Services with higher dependencies take precedence in location selection. Our experimental investigation involves illustrative cases where we compare our results with baseline strategies and genetic algorithms. We observed a comparable Pareto set with negligible execution times, measured in the order of milliseconds, in contrast to the hours required by alternative approaches.

Discrete Black Widow Optimization Algorithm for Multi-Objective IoT Application Placement in Fog Computing Environments

Discrete Black Widow Optimization Algorithm for Multi-Objective IoT Application Placement in Fog Computing Environments

Joint optimization of application placement and resource allocation for enhanced performance in heterogeneous multi-server systems

Efficiently placing applications remains a critical challenge across diverse multi-server environments, including web hosting centers, cloud computing, and edge computing environments. Unfortunately, most existing studies tend to overlook the crucial aspect of resource allocation, leading to suboptimal system performance. To address this gap, there is a pressing need to holistically explore both application placement and resource allocation in a unified manner. In this paper, we introduce the place and allocate problem in heterogeneous multi-server systems, a novel approach aiming at simultaneously optimizing the placement and allocation of applications to maximize the overall utility of the heterogeneous multi-server system. Our proposed methodology harnesses the interplay between application placement and resource allocation, showcasing substantial improvements in system utility. To model individual application performance concerning their allocated resources, we employ utility functions. For concave utility functions, we present an approximation algorithm that operates efficiently with a time complexity of O(mn3(logC)2), where n represents the number of applications, m is the number of servers, and C denotes the maximum available resource capacity of each server. Furthermore, we extend our approach to accommodate more general scenarios that involve applications with nonconcave utility functions and using multiple types of resources. Our study includes comprehensive experimental evaluations conducted on applications with both synthetic and real-world utility functions. Results consistently showcase that our algorithms achieve over 96.9% of optimal performance on average. Additionally, comparative analysis against several practical heuristics reveal that our algorithms outperform these methods by up to 4.3 times in total utility.

Analgesia and Anesthesia Practice Patterns for Gynecologic Brachytherapy Procedures and Potential Impact on Women's Procedural Experience: A National Survey

PurposeTo determine the current U.S. practice patterns of analgesia (AG) and anesthesia (AS) for gynecologic brachytherapy (BT) procedures. Methods and MaterialsA 27-item survey created with expertise from five brachytherapists was distributed electronically to 90 U.S. Radiation Oncology academic programs and publicized on social media and at two national meetings during June-October 2023. ResultsForty-one responses were received (46%). Fifty-four percent identified as female, 66% Caucasian and 85% non-Hispanic/Latino ethnicity. Forty nine percent utilize a BT suite +/- CT simulator alone, 39% the operating room +/- BT suite or CT simulator or other location, 10% CT simulation room alone, and 2% clinic exam room. Thirty four percent utilize general anesthesia alone (GA) for intracavitary BT (n=41), 20% conscious sedation (CS) alone, 10% oral analgesia (OA) alone, and 9% spinal or epidural AS alone, and 27% combination. Among those performing hybrid BT (n=25), 40% use GA alone, 16% use CS alone, 12% epidural or spinal AS alone, and 4% OA alone, and 28% combination. For template interstitial BT (n=25), 44% use GA alone, 48% epidural alone or in combination with other AS, and 8% CS alone. Twenty-two percent of respondents provide AG or AS during applicator placement only, while 32% provide it during placement, planning, treatment, and removal. The most common reasons for not using CS or GA were lack of anesthesia resources and clinician preference. Seventy-three percent reported the belief that patients suffer from post-traumatic stress disorder (PTSD) symptoms after BT. However, 68% reported not using techniques to alleviate BT related emotional distress. ConclusionsMany U.S. brachytherapists report using GA, CS or epidural AS, yet 10% are using only OA and 22% offer AG/AS only during applicator placement. Furthermore, a majority of respondents believe PTSD symptoms can occur after BT, but few offer any intervention. AS resources and clinician preference should be targeted for expansion of higher quality care.

The Impact of the COVID-19 Pandemic on Geographic Placement of Applicants to General Surgery Residency Programs

COVID-19 greatly influenced medical education and the residency match. As new guidelines were established to promote safety, travel was restricted, visiting rotations discontinued, and residency interviews turned virtual. The purpose of this study is to assess the geographic trends in distribution of successfully matched General Surgery applicants prior to and after the implementation of pandemic guidelines, and what we can learn from them as we move forward. This was a retrospective review of 129 Accreditation Council for Graduate Medical Education (ACGME) accredited, academic General Surgery Residency Programs across 46 states and the District of Columbia. Categorically matched residents' medical schools (i.e., home institutions), medical school states, and medical school regions as defined per the Association of American Medical Colleges (AAMC), were compared to the same geographic datapoints as their residency program. Preliminary residents were excluded. Residents in the 2018, 2019, and 2020 cycles were sub-categorized into the "pre-COVID" group and residents in the 2021 and 2022 applications cycles were sub-categorized into the "post-COVID" group. The percentages of residents who matched at their home institution, in-state, and in-region were examined. Multiple ACGME-accredited, university-affiliated General Surgery Residency Programs across the United States of America. A total of 4033 categorical General Surgery residents were included. Of 4033 categorical residents who matched between 2018 and 2022, 56.1% (n = 2,263) were in the pre-COVID group and 43.9% (n = 1770) were in the post-COVID group. In the pre-COVID group 14.4% (n = 325) of residents remained in-home (IH), 24.4% (n = 553) in-state (IS), and 37.0% (n = 837) in- region (IR), compared to 18.8% IH (n = 333), 27.8% IS (n = 492), and 39.9% IR (n = 706) in the post-COVID group, respectively. Significant increases for IH and IS resident matching at 4.5% and 3.4%, respectively, were noted in the post-COVID period (p < 0.05). The COVID-19 pandemic, and the ensuing changes adopted to promote safety, significantly impacted medical student opportunities and the General Surgery residency application process. General Surgery match data over the last 5 years reveals a statistically significant increase in the percentage of applicants matching at in-home and in-state institutions after the pandemic.

Reproducibility and air gap pockets of 3D-printed brachytherapy applicator placement in high-dose-rate skin cancer

Background and PurposeThis study aimed to investigate the reproducibility of a novel approach using 3D printed brachytherapy applicators for the treatment of skin cancer. Specifically, we aimed to assess the accuracy of applicator placement and to minimize the existence of air gap pockets between the applicator and the patient’s skin. Materials and MethodsA total of 20 patients plans diagnosed with skin cancer were enrolled in this study. All patients underwent high dose rate (HDR) brachytherapy. To ensure precise applicator placement, patient-specific 3D printed applicators were designed based on individual body and tumor topography, utilizing data obtained from computer tomography (CT) scans. All applicators were fabricated using fused deposition modeling technology. ResultsThe error in applicator placement was measured and found to be less than 1.0 mm on average, with a standard deviation of 0.9 mm. Additionally, the average error in air gap pockets between the applicator and the patient’s skin was 0.4 mm (standard deviation was 0.5 mm). The study demonstrated that the personalized approach of 3D printed brachytherapy applicator placement in skin cancer treatment yielded highly accurate results. The average error of less than 1.0 mm in applicator positioning and the minimal air gap pockets demonstrated the reproducibility and precision of this technique. ConclusionOur study establishes the reproducibility and accuracy of 3D-printed brachytherapy applicator placement in the treatment of skin cancer. This personalized treatment approach offers a highly precise method for delivering radiation therapy, minimizing the risk to adjacent healthy tissues, and enhancing overall patient outcomes.

Ultrasound and Photoacoustic Imaging for the Guidance of Laser Ablation Procedures.

The accuracy and efficacy of laser ablation procedures depend on the accurate placement of the laser applicator within the diseased tissue, monitoring the real-time temperature during the ablation procedure, and mapping the extent of the ablated region. Ultrasound (US) imaging has been widely used to guide ablation procedures. While US imaging offers significant advantages for guiding ablation procedures, its limitations include low imaging contrast, angular dependency, and limited ability to monitor the temperature. Photoacoustic (PA) imaging is a relatively new imaging modality that inherits the advantages of US imaging and offers enhanced capabilities for laser-guided ablations, such as accurate, angle-independent tracking of ablation catheters, the potential for quantitative thermometry, and monitoring thermal lesion formation. This work provides an overview of ultrasound-guided procedures and how different US-related artifacts limit their utility, followed by introducing PA as complementary to US as a solution to address the existing limitations and improve ablation outcomes. Furthermore, we highlight the integration of PA-driven features into existing US-guided laser ablation systems, along with their limitations and future outlooks. Integrated US/PA-guided laser ablation procedures can lead to safer and more precise treatment outcomes.

Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimization

The increasing complexity of Compute Continuum environments calls for efficient resource optimization techniques. In this paper, we propose and evaluate three distributed designs of a genetic algorithm (GA) for resource optimization, within an increasing degree of distribution. The designs leverage the execution of the GA in the infrastructure devices themselves by dealing with the specific features of this domain: constrained resources and wide geographical distribution of the devices. For their evaluation, we implemented a benchmark case using the NSGA-II for the specific problem of optimizing the application placement, according to the guidelines of our three distributed designs. These three experimental scenarios were compared against a control case, representing a traditional centralized version of this GA algorithm, evaluating solution quality and network overhead. The results show that the design with the lowest distribution degree, which keeps centralized storage of the objective space, achieves comparable solution quality to the traditional approach but incurs a higher network load. The second design, which completely distributes the population between the workers, reduces network overhead but exhibits lower solution diversity while keeping enough good results in terms of optimization objective minimization. The second design demonstrates the highest overall efficiency in optimization performance and network cost. Finally, the proposal with a distributed population that only interchanges solutions between the workers’ neighbors achieves the lowest network load but with compromised solution quality.

Optimizing Service Placement and Enhancing Service Allocation for Microservice Architectures in Cloud Environments

With the increasing popularity of microservice architecture, there is a growing need to deploy service-based applications efficiently in cloud environments. Traditional cluster schedulers often fail to optimize service placement adequately, as they only consider resource constraints and overlook traffic demands between services. This oversight can lead to performance issues such as high response times and jitter. To address this challenge, we propose a novel approach to optimize the placement of service-based applications in clouds. Our approach involves partitioning the application into segments while minimizing overall traffic between them, and then strategically allocating these segments to machines based on their resource and traffic demands. We have developed a prototype scheduler and conducted extensive experiments on test bed clusters to evaluate its performance. The results demonstrate that our approach surpasses existing container cluster schedulers and heuristic methods, significantly reducing overall inter-machine traffic and improving application performance

Dosimetric Analysis of Computed Tomography-Based Brachytherapy Planning in Carcinoma Cervix

Background The standard of care for locally advanced cervical cancer is concurrent chemoradiation followed by intracavitary brachytherapy (BT). BT forms an integral part of management as it improves local control and overall survival. In recent times, image-guided BT (IGBT) has been recommended as the standard of care. Computed tomography (CT) scan-based BT is a cost-effective and easily available modality for IGBT. The aim of the study was to do a dosimetric analysis of CT scan-based BT for patients with cervical cancer. Methods This was a retrospective study and included patients with cervical cancer treated with radical chemoradiation followed by BT. CT scan was done before every fraction after applicator placement, and CT-based planning was done for all fractions. Clinical details were abstracted from the case records, and dosimetric details were collected from the treatment planning systems. Total equivalent dose in 2 Gy per fraction (EQD2) was calculated for external beam radiation therapy and BT target volumes and organs at risk (OARs). Results This study included 50 patients. The mean age was 45 years. The majority of the cases were stage III. The mean high-risk clinical target volume (HRCTV) for the 1st, 2nd, and 3rd fraction was 53.1, 52.1, and 51.3 mL, respectively. Mean D90 HRCTV (dose received by 90% of the HRCTV) dose was 81.4 Gy (EQD2 10 Gy) and D2cc was 75.8 Gy (EQD2 3 Gy) for the rectum and sigmoid and 86.2 Gy (EQD2 3 Gy) for the bladder. Conclusion CT-based BT is a reasonable option in high-volume and low-resource settings where the availability of magnetic resonance imaging is limited. CT-based BT at every fraction can ensure proper applicator placement and aid in optimizing the dose to the target volumes and OARs.

Preclinical assessment of a noncooled MR thermometry-based neurosurgical laser therapy system.

MRI-guided laser interstitial thermal therapy (MRgLITT) has recently gained interest as an ablative stereotactic procedure for intractable epilepsy, movement disorders, and brain tumors. Conventionally, a LITT system consists of a laser generator and cooled laser applicator, which is a fiber optic core surrounded by a sheath through which cooled fluid is pumped. However, this footprint can make the system bulky and nonmobile, limit the maximum depth of targeting, and increase the chances of breakdown. Herein, the authors conduct a preclinical assessment of a noncooled MRgLITT system in a porcine model. Three-tesla MRI was used to guide the in vivo placement of noncooled laser applicators in the porcine brain. The study consisted of a survival arm and terminal arm. The laser was activated at a power of 4-7 W for ≤ 180 seconds. Temperature changes were monitored using the MR thermometry software ThermoGuide in the survival arm (n = 5) or both ThermoGuide software and adjacently inserted thermal probes in the terminal arm (n = 3). Thermal damage was determined by the software using the temperature-time relationship of cumulative equivalent minutes at 43°C (CEM43). Temperatures calculated by the software were compared with those recorded by the temperature probes. The dimensions of thermal damage thresholds (TDTs; 2-9, 10-59, 60-239, ≥ 240 CEM43 isolines) given by MR thermometry were compared with the dimensions of irreversible damage on histopathological analysis. There was a strong correlation between temperature recordings by ThermoGuide and those by thermal probes at both 4 mm (r = 0.96) and 8 mm (r = 0.80), with a mean absolute error of 0.76°C ± 2.13°C and 0.17°C ± 1.65°C at 4 and 8 mm, respectively. The area of 2-9 CEM43 was larger than the area of irreversible damage seen on histopathological analysis. The dimensions of the 10 and 60 CEM43 correlated well with dimensions of the lesion on histopathological analysis. A well-defined border (≤ 1 mm) was observed between the area of irreversible damage and healthy brain tissue. This preclinical assessment showed that the noncooled LITT system was able to precisely reach the target and create well-defined lesions within a margin of safety, without any adverse effects. MR thermometry software provided an accurate near-real-time temperature of the brain tissue, and dimensions of the lesion as visualized by the software correlated well with histopathological findings. Further studies to test the system's efficacy and safety in human subjects are in progress.

Latency-Energy Aware Dynamic Application Placement for Edge Computing: A Vacation Queue Based Optimization Approach

Latency-Energy Aware Dynamic Application Placement for Edge Computing: A Vacation Queue Based Optimization Approach

Retraction: A study on expression level and nutritional status of IGFBP-2 after left neck anastomosis combined with placement of feeding nutritional applicators carrying ^(125)I particles in the treatment of esophageal cancer.

[This retracts the article DOI: 10.3389/fonc.2022.1085305.].

A robust optimization approach for placement of applications in edge computing considering latency uncertainty

Edge computing brings computing and storage resources close to end-users to support new applications and services that require low network latency. It is currently used in a wide range of industries, from industrial automation and augmented reality, to smart cities and connected vehicles, where low latency, data privacy, and real-time processing are critical requirements. The latency of accessing applications in edge computing must be consistently below a threshold of a few tens of milliseconds to maintain an acceptable experience for end-users. However, the latency between users and applications can vary considerably depending on the network load and mode of wireless access. An application provider must be able to guarantee that requests are served in a timely manner by their application instances hosted in the edge despite such latency variations. This article focuses on the placement and traffic allocation problem faced by application providers in determining where to place application instances on edge nodes such that requests are served within a certain deadline. It proposes novel formulations based on robust optimization to provide optimal plans that protect against latency variations in a configurable number of network links. The robust formulations are based on two different types of polyhedral uncertainty sets that offer different levels of protection against variations in latency. Extensive simulations show that our robust models are able to keep the number of chosen edge nodes low while reducing the number of latency violations as compared to a deterministic optimization model that only considers the average latency of network links.

Opposition‐based improved memetic algorithm for placement of concurrentInternet of Thingsapplications in fog computing

AbstractThe transpiration of a new computing standard, Fog computing exploits the computing resources immediacy to the Internet of Things (IoT) devices and hence together with the cloud servers bestow the required resources promptly. Even though the fog server provides timely services due to the fewer resources it cannot tackle the resource‐consuming applications developed by more IoT devices. Hence the placement of applications in the fog computing paradigm with more cloud and fog servers is a major challenging problem. To overcome this problem and to minimize the execution time and energy consumption of the IoT applications in a computing platform consisting of numerous IoT devices, and several fog and cloud servers, a weighted cost model is proposed. The number of parallel jobs for concurrent execution is increased due to the heterogeneity of IoT applications, and hence a pre‐scheduling technique is presented to accomplish this. In addition to that, a novel application placement method using the Opposition‐based Improved Memetic Algorithm (OBIMA) is adapted to formulate the placement of parallel IoT operations. The experimental results show that the proposed model outperforms well compared to other state‐of‐the‐art methods.

An Efficient and Autonomous Planning Scheme for Deploying IoT Services in Fog Computing: A Metaheuristic-Based Approach

The fog computing paradigm is a promising concept to overcome the exponential increase in data volume in Internet of Things (IoT) applications. This paradigm can support delay-sensitive IoT applications by extending cloud services to the network edge. However, fog computing faces challenges such as resource allocation for applications at the network edge due to limited resources as well as its heterogeneous and distributed nature. This is in line with the goals of microservice architecture and develops the placement of microservice-based IoT applications. The IoT service placement problem (SPP) on fog nodes is known as non-deterministic polynomial-time (NP)-hard. In this study, we introduce a meta-heuristic approach named SPP-differential evolution algorithm (DEA) to handle SPP, which originates from the DEA with a shared parallel architecture. The proposed method takes advantage of the scalable and deployable nature of microservices to minimize the resource utilization and delay as much as possible. SPP-DEA is developed based on monitoring, analysis, decision-making, and execution with knowledge bas (MADE-k) autonomous planning model with the aim of compromise between service cost, response time, resource utilization, and throughput. In order to address the computational complexity of the problem, we consider the resource consumption distribution and service deployment priority in the placement process. In order to evaluate the quality of placement in SPP-DEA, extensive experiments have been performed on a synthetic fog environment. The simulation results show that compared to the state-of-the-art approaches, SPP-DEA reduces the service cost and waiting time by 16% and 11%, respectively.

Encapsulation of garlic oil and diallyl disulfide with β-cyclodextrin for white rot control in Allium crops

White rot, caused by Sclerotium cepivorum, is a devastating disease of onion, garlic, and other Allium crops. The pathogen reproduces and survives by forming sclerotia, which can survive in field soils for decades. The pathogen is host-specific to Allium spp., and sclerotia germinate in response to sulfur compounds produced by Allium roots. Sulfur-containing compounds such as diallyl disulfide (DADS) and garlic oil can stimulate sclerotia germination and effectively reduce pathogen populations before planting an Allium crop; however, these compounds are often highly volatile and difficult to handle and apply. Therefore, a study was conducted to encapsulate DADS and garlic oil with β-cyclodextrin (β-CD) to protect sulfur volatiles from interactions with soil and against oxidation, degradation, and evaporation. Microcapsule extract analysis showed that garlic oil and DADS were successfully encapsulated with greater than 90% recovery of the starting product. In addition, the micro-encapsulation process did not change the chemical compounds, but their proportions were altered. Furthermore, lab and growth chamber incubation experiments revealed that DADS and DAS could be slowly released into the soil from microcapsules and worked effectively for white rot sclerotia germination. Compared to the non-treated control, liquid DADS, microcapsules of DADS, and microcapsules of garlic oil reduced sclerotia populations by between 73.7 and 82.3%, depending on the treatment. When compared to liquid DADS and garlic oil, the encapsulated formulations DADS and garlic oil are equally effective but easier to handle, which can provide more flexibility in terms of timing, placement, and method of sclerotia germination stimulant applications.